THESIS
On reactive visual analytics on heterogeneous data streams
Nowadays, in different contexts (e.g. Smart Cities or Disaster Management), the needs of reactive sense and decision making are growing along with the number of heterogenoeous data stream to analyze. In the last decade, these needs were investigated from different point of views. Visual analytics is a disruptive approach to data analysis centered on the abilities of sense and decision makers to visually perform correlation and comparison among data. The limit of visual analytics is in the need of homogeneity in the data. The state of the art for integrating big heterogeneous data is represented by Ontology Based Data Access (OBDA) techniques, where a Conceptual Integrated Model (CIM) creates a logically homogeneous layer on top of heterogenous data sources. The state of the art for analysing data streams are DSMS and CEP, that demonstrated the need for a paradigmatic change from one time to continuous semantic. A first attempt to fuse OBDA and Stream Processing is undergoing in the stream reasoning field, but many challenges remain open and unexplored. To make an effort to put together these different point of views, my research question is: is it possible to perform reactive visual analytics on thousands heterogeneous data streams for sense and decision making? A deep state of the art investigation represents the starting point of my activity. The definition of different use cases is the next step of my work. Then I aim to: 1) develop a CIM to create useful abstractions for visual analytics processes, 2) specify a language and engineer a system able to bridge the gap between thousands data streams and few homogeneous visual abstractions and 3) set up an environment for comparative research in order to demonstrate that the proposed language and system is able to address real problems identified in the use cases.
Advisor: Emanuele Della Valle